Automated biometric sensors are used to establish or authenticate the identity of a person based on biological characteristics that are in possession of the person. One example of such biometric modality is fingerprint recognition. Various types of fingerprint recognition sensors are known, including optical sensors, capacitance sensors, thermal sensors, and ultrasonic sensors.
Current methods of data acquisition use quantitative measurements of forces that an individual applies to a fingerprint sensor to improve the acquisition process. A lookup table of force measurements is created for multiple fingerprint sensing technologies in order to acquire the highest quality fingerprint images. The optimal finger force to produce the best results varies by fingerprint sensing technology.
A fingerprint acquisition algorithm relies on a constant monitoring of forces applied to a fingerprint sensor by the user's finger. This monitoring allows for acquisition of the fingerprint image to occur when the force level is in an optimal range. The optimal range is illustratively listed in a lookup table accessible by the device. The acquisition algorithm also provides feedback/guidance to the user on how much pressure to apply on the device by comparing the actual force applied to the optimal pressure value for the particular device retrieved from the lookup table. Primary applications of such systems include, but are not limited to: law enforcement, registered traveler programs, financial services, healthcare, telecommunications, social services, electronic commerce, and access control.
Providing a lookup table of force measurements for different fingerprint sensor technologies, and providing constructive feedback to the user resulted in improved quality of the fingerprint images themselves acquired by the fingerprint recognition systems. These improvements lead to improved matching performance. Image quality is a predictor of matching performance for detection and recognition systems. Knowledge of the force level significantly increases the average reported image quality score by about 20% if knowledge of the fingerprint sensing technology and applied force is known. Correspondingly, fingerprint matching performance can improve over 10% for optical and capacitance technologies if knowledge of the fingerprint sensing technology and applied force is known.
Further, such systems and methods analyzed the impact of human interaction with fingerprint sensors and the implications on image quality and subsequent algorithm performance. The significance of user interaction with various fingerprint recognition sensor technologies is apparent, given that fingerprint recognition is the most widely used of the biometric technologies, with popular applications in law enforcement (e.g., the Integrated Automated Fingerprint Identification System—IAFIS), access control, time and attendance recordkeeping, and personal computer/network access. Fingerprint identification is also used with personal data assistants, mobile phones, laptop computers, desktop keyboards, mice, and universal serial bus (USB) flash media drives.
However, even given these improvements in biometric data acquisition methods, there is still an unmet need for a feedback technology that goes beyond mere improvements in methods of biometric data recognition in order to provide improved accessibility and ease of use for users with disabilities, increased throughput, increased image quality, increased performance, an ability for achieving optimal force levels (e.g., by providing a number), and stability determination (which will aid in determining whether the subject is at their optimal performance).
Examples of the current ways in which the aforementioned unmet needs are currently being addressed include, but are not limited to: using basic signage at the border and on certain devices being utilized and requiring the immigration officer (if, e.g., the device is being used at the border) to provide the feedback, which by its nature is very limited. Clearly, therefore, a more robust improvement is needed to allow for modality neutral methods of biometric sensing that goes beyond mere fingerprint reading.